基于 MRI 的动力学异质性评估在使用混合 CNN-RNN 模型准确获取乳腺癌腋窝淋巴结状态中的应用。

MRI-Based Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid CNN-RNN Model.

机构信息

Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin's Clinical Research Center for Cancer, Tianjin, China.

School of Biomedical Engineering & Technology, Tianjin Medical University, Tianjin, China.

出版信息

J Magn Reson Imaging. 2024 Oct;60(4):1352-1364. doi: 10.1002/jmri.29225. Epub 2024 Jan 11.

DOI:10.1002/jmri.29225

PMID:38205712

Abstract

BACKGROUND

Accurate evaluation of the axillary lymph node (ALN) status is needed for determining the treatment protocol for breast cancer (BC). The value of magnetic resonance imaging (MRI)-based tumor heterogeneity in assessing ALN metastasis in BC is unclear.

PURPOSE

To assess the value of deep learning (DL)-derived kinetic heterogeneity parameters based on BC dynamic contrast-enhanced (DCE)-MRI to infer the ALN status.

STUDY TYPE

Retrospective.

SUBJECTS

1256/539/153/115 patients in the training cohort, internal validation cohort, and external validation cohorts I and II, respectively.

FIELD STRENGTH/SEQUENCE: 1.5 T/3.0 T, non-contrast T1-weighted spin-echo sequence imaging (T1WI), DCE-T1WI, and diffusion-weighted imaging.

ASSESSMENT

Clinical pathological and MRI semantic features were obtained by reviewing histopathology and MRI reports. The segmentation of the tumor lesion on the first phase of T1WI DCE-MRI images was applied to other phases after registration. A DL architecture termed convolutional recurrent neural network (ConvRNN) was developed to generate the KH (kinetic heterogeneity of DCE-MRI image) score that indicated the ALN status in patients with BC. The model was trained and optimized on training and internal validation cohorts, tested on two external validation cohorts. We compared ConvRNN model with other 10 models and the subgroup analyses of tumor size, magnetic field strength, and molecular subtype were also evaluated.

STATISTICAL TESTS

Chi-squared, Fisher's exact, Student's t, Mann-Whitney U tests, and receiver operating characteristics (ROC) analysis were performed. P < 0.05 was considered significant.

RESULTS

The ConvRNN model achieved area under the curve (AUC) of 0.802 in the internal validation cohort and 0.785-0.806 in the external validation cohorts. The ConvRNN model could well evaluate the ALN status of the four molecular subtypes (AUC = 0.685-0.868). The patients with larger tumor sizes (>5 cm) were more susceptible to ALN metastasis with KH scores of 0.527-0.827.

DATA CONCLUSION

A ConvRNN model outperformed traditional models for determining the ALN status in patients with BC.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

摘要

背景

准确评估腋窝淋巴结（ALN）状态对于确定乳腺癌（BC）的治疗方案至关重要。磁共振成像（MRI）基于肿瘤异质性在评估 BC 中 ALN 转移中的价值尚不清楚。

目的

评估基于 BC 动态对比增强（DCE）MRI 的深度学习（DL）衍生的动力学异质性参数来推断 ALN 状态的价值。

研究类型

回顾性。

受试者

分别在训练队列、内部验证队列和外部验证队列 I 和 II 中纳入 1256/539/153/115 例患者。

磁场强度/序列：1.5T/3.0T，非对比 T1 加权自旋回波序列成像（T1WI）、DCE-T1WI 和弥散加权成像。

评估

通过回顾组织病理学和 MRI 报告获得临床病理和 MRI 语义特征。在注册后将肿瘤病变在 T1WI DCE-MRI 图像的第一期的分割应用于其他期。开发了一种称为卷积循环神经网络（ConvRNN）的 DL 架构来生成 DCE-MRI 图像的 KH（动力学异质性）评分，该评分指示 BC 患者的 ALN 状态。该模型在训练和内部验证队列上进行了训练和优化，并在两个外部验证队列上进行了测试。我们将 ConvRNN 模型与其他 10 个模型进行了比较，并评估了肿瘤大小、磁场强度和分子亚型的亚组分析。

统计学检验

进行了卡方检验、Fisher 精确检验、学生 t 检验、Mann-Whitney U 检验和受试者工作特征（ROC）分析。P<0.05 被认为具有统计学意义。

结果

ConvRNN 模型在内部验证队列中的曲线下面积（AUC）为 0.802，在外部验证队列中为 0.785-0.806。ConvRNN 模型能够很好地评估四种分子亚型的 ALN 状态（AUC=0.685-0.868）。肿瘤较大（>5cm）的患者更易发生 ALN 转移，KH 评分在 0.527-0.827 之间。